Mapping with Geopandas

Mapping is a GIS specialization unto itself, but in this reading I want to provide a really quick intro to some of the tools available for mapping data with geopandas.

plot()

As we saw in our previous readings, the .plot() command is an easy way to create maps in geopandas. Just running .plot() will result in geopandas visualizing our data:

import geopandas as gpd

world = gpd.read_file(
    "https://github.com/nickeubank/practicaldatascience_book/"
    "raw/refs/heads/main/notebooks/"
    "gis/example_data/world_countries/world_countries_robinson.geojson"
)

# Plot!
world.plot()

<Axes: >

Moreover, as we saw in our intro reading, if you pass .plot() a column name, geopandas will color each observation based on the value of that variable to create something called a choropleth map:

world["gdp_per_cap"] = world["gdp_md"] / world["pop_est"]
world = world[world["name"] != "Antarctica"].copy()
world.plot("gdp_per_cap", cmap="Reds")

<Axes: >

(Note the geopandas .plot() method also accepts all the usual arguments you might pass to matplotlib, like cmap, which controls the colors used in the plot).

Native Interactive Maps

But perhaps the coolest mapping trick that is provided by geopandas is the ability to create interactive maps just using the .explore() method!

world.explore()

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Which can also be combined with lots of options:

world.explore(
    column="pop_est",
    scheme="naturalbreaks",  # use mapclassify's natural breaks scheme
    legend=False,  # show legend
    k=10,  # use 10 bins
    name="countries",  # name of the layer in the map
    cmap="Reds",
)

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And of course you can also layer maps on top of one another:

cities = gpd.read_file(
    "https://github.com/nickeubank/practicaldatascience_book/raw"
    "/refs/heads/main/notebooks/gis/example_data/world_cities/world_cities_robinson.geojson"
)

import folium

# Assign map object to m
m = world.explore(
    column="pop_est",
    scheme="naturalbreaks",
    legend=False,
    k=10,
    name="countries",
    cmap="Reds",
)

cities.explore(
    m=m,  # pass map you want to add to here.
    color="red",  # use red color on all points
    marker_kwds=dict(radius=5, fill=True),  # make marker radius 10px with fill
    tooltip="pop_max",  # show "name" column in the tooltip
    tooltip_kwds=dict(labels=False),  # do not show column label in the tooltip
    name="Cities",  # name of the layer in the map
)

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To learn more, you can read the full docs here! and/or try it yourself!